{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第3章 单因子探索分析与可视化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.HR人力资源数据集分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.stats as ss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "HR_PATH = \"./data/HR.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_monthly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>department</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14994</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2</td>\n",
       "      <td>151</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.96</td>\n",
       "      <td>6</td>\n",
       "      <td>280</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "14994                0.40             0.57               2   \n",
       "14995                0.37             0.48               2   \n",
       "14996                0.37             0.53               2   \n",
       "14997                0.11             0.96               6   \n",
       "14998                0.37             0.52               2   \n",
       "\n",
       "       average_monthly_hours  time_spend_company  Work_accident  left  \\\n",
       "14994                    151                   3              0     1   \n",
       "14995                    160                   3              0     1   \n",
       "14996                    143                   3              0     1   \n",
       "14997                    280                   4              0     1   \n",
       "14998                    158                   3              0     1   \n",
       "\n",
       "       promotion_last_5years department salary  \n",
       "14994                      0    support    low  \n",
       "14995                      0    support    low  \n",
       "14996                      0    support    low  \n",
       "14997                      0    support    low  \n",
       "14998                      0    support    low  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(HR_PATH)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 satisfaction_level列分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_sl = df['satisfaction_level']  # satisfaction_level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        False\n",
       "1        False\n",
       "2        False\n",
       "3        False\n",
       "4        False\n",
       "         ...  \n",
       "14994    False\n",
       "14995    False\n",
       "14996    False\n",
       "14997    False\n",
       "14998    False\n",
       "Name: satisfaction_level, Length: 14999, dtype: bool"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_sl.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: satisfaction_level, dtype: float64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_sl[s_sl.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_monthly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>department</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [satisfaction_level, last_evaluation, number_project, average_monthly_hours, time_spend_company, Work_accident, left, promotion_last_5years, department, salary]\n",
       "Index: []"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['satisfaction_level'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_sl=s_sl.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6128335222348156"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_sl.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.24863065106114257"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_sl.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_sl.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.09"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_sl.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_sl.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.44"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_sl.quantile(q=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.82"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_sl.quantile(q=0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.4763603412839644"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_sl.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.6708586220574557"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_sl.kurt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 195, 1214,  532,  974, 1668, 2146, 1972, 2074, 2220, 2004],\n",
       "       dtype=int32),\n",
       " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 连续型变量，离散化得到分布\n",
    "np.histogram(s_sl.values,  bins=np.arange(0.0, 1.1, 0.1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 last_evaluation列分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_le = df['last_evaluation']  # last_evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        False\n",
       "1        False\n",
       "2        False\n",
       "3        False\n",
       "4        False\n",
       "         ...  \n",
       "14994    False\n",
       "14995    False\n",
       "14996    False\n",
       "14997    False\n",
       "14998    False\n",
       "Name: last_evaluation, Length: 14999, dtype: bool"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_le.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: last_evaluation, dtype: float64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_le[s_le.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7161017401160078"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_le.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.17116911062327533"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_le.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.72"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_le.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_le.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.36"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_le.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.02662174986376086"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_le.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.2390402819304127"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_le.kurt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: last_evaluation, dtype: float64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_le[s_le>1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_le=s_le[s_le<=1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 去除异常值\n",
    "q_low = s_le.quantile(q=0.25)\n",
    "q_high = s_le.quantile(q=0.75)\n",
    "q_interval = q_high - q_low\n",
    "k=1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0.53\n",
       "1        0.86\n",
       "2        0.88\n",
       "3        0.87\n",
       "4        0.52\n",
       "         ... \n",
       "14994    0.57\n",
       "14995    0.48\n",
       "14996    0.53\n",
       "14997    0.96\n",
       "14998    0.52\n",
       "Name: last_evaluation, Length: 14999, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_le=s_le[s_le<q_high+k*q_interval][s_le>q_low-k*q_interval]\n",
    "s_le"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14999"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s_le)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   0,    0,    0,  179, 1389, 3395, 2234, 2062, 2752, 2988],\n",
       "       dtype=int32),\n",
       " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]))"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 连续型变量，离散化得到分布\n",
    "np.histogram(s_le.values, bins=np.arange(0.0, 1.1, 0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7161017401160078"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_le.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.17116911062327533"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_le.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.72"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_le.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_le.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.36"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_le.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.02662174986376086"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_le.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.2390402819304127"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_le.kurt()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 number_project列分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_np = df['number_project']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: number_project, dtype: int64)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_np[s_np.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.80305353690246"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_np.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2325923553183522"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_np.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_np.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_np.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_np.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3377056123598222"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_np.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.4954779519008947"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_np.kurt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    4365\n",
       "3    4055\n",
       "5    2761\n",
       "2    2388\n",
       "6    1174\n",
       "7     256\n",
       "Name: number_project, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2-7,可以直接数数据量确认分布\n",
    "s_np.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    0.291019\n",
       "3    0.270351\n",
       "5    0.184079\n",
       "2    0.159211\n",
       "6    0.078272\n",
       "7    0.017068\n",
       "Name: number_project, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_np.value_counts(normalize=True)  # 直接获得构成比率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    0.159211\n",
       "3    0.270351\n",
       "4    0.291019\n",
       "5    0.184079\n",
       "6    0.078272\n",
       "7    0.017068\n",
       "Name: number_project, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_np.value_counts(normalize=True).sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 average_monthly_hours列分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_amh = df['average_monthly_hours']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "201.0503366891126"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_amh.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49.94309937128408"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_amh.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "310"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_amh.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "96"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_amh.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0528419894163242"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_amh.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.1349815681924558"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_amh.kurt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        157\n",
       "1        262\n",
       "2        272\n",
       "3        223\n",
       "4        159\n",
       "        ... \n",
       "14994    151\n",
       "14995    160\n",
       "14996    143\n",
       "14997    280\n",
       "14998    158\n",
       "Name: average_monthly_hours, Length: 14999, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q_low = s_amh.quantile(0.25)\n",
    "q_high = s_amh.quantile(0.75)\n",
    "q_interval = q_high - q_low\n",
    "k = 1.5\n",
    "s_amh=s_amh[s_amh<q_high+k*q_interval][s_amh>q_low-k*q_interval]\n",
    "s_amh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 367, 1240, 2733, 1722, 1628, 1712, 1906, 2240, 1127,  324],\n",
       "       dtype=int32),\n",
       " array([ 96. , 117.4, 138.8, 160.2, 181.6, 203. , 224.4, 245.8, 267.2,\n",
       "        288.6, 310. ]))"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.histogram(s_amh.values, bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 168,  171,  147,  807, 1153, 1234, 1072,  824,  818,  758,  751,\n",
       "         738,  856,  824,  987, 1002, 1045,  935,  299,  193,  131,   86],\n",
       "       dtype=int32),\n",
       " array([ 96, 106, 116, 126, 136, 146, 156, 166, 176, 186, 196, 206, 216,\n",
       "        226, 236, 246, 256, 266, 276, 286, 296, 306, 316], dtype=int64))"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.histogram(s_amh.values, bins=np.arange(s_amh.min(), s_amh.max()+10, 10))  # 左闭右开"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(146.0, 156.0]     1277\n",
       "(136.0, 146.0]     1159\n",
       "(256.0, 266.0]     1063\n",
       "(236.0, 246.0]     1006\n",
       "(156.0, 166.0]      992\n",
       "(246.0, 256.0]      987\n",
       "(126.0, 136.0]      886\n",
       "(216.0, 226.0]      873\n",
       "(266.0, 276.0]      860\n",
       "(166.0, 176.0]      832\n",
       "(226.0, 236.0]      814\n",
       "(176.0, 186.0]      813\n",
       "(186.0, 196.0]      761\n",
       "(196.0, 206.0]      755\n",
       "(206.0, 216.0]      731\n",
       "(276.0, 286.0]      319\n",
       "(95.999, 106.0]     187\n",
       "(286.0, 296.0]      164\n",
       "(116.0, 126.0]      162\n",
       "(106.0, 116.0]      162\n",
       "(296.0, 306.0]      128\n",
       "(306.0, 316.0]       68\n",
       "Name: average_monthly_hours, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_amh.value_counts(bins=np.arange(s_amh.min(), s_amh.max()+10, 10))  # 左开右闭"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5 time_spend_company列分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_tsc = df['time_spend_company']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.498233215547703"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_tsc.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4601362305354812"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_tsc.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     3244\n",
       "3     6443\n",
       "4     2557\n",
       "5     1473\n",
       "6      718\n",
       "7      188\n",
       "8      162\n",
       "10     214\n",
       "Name: time_spend_company, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_tsc.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.6 work_accident列分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['satisfaction_level', 'last_evaluation', 'number_project',\n",
       "       'average_monthly_hours', 'time_spend_company', 'Work_accident', 'left',\n",
       "       'promotion_last_5years', 'department', 'salary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_wa = df['Work_accident']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    12830\n",
       "1     2169\n",
       "Name: Work_accident, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_wa.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1446096406427095"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_wa.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.7 left列分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_l= df['left']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    11428\n",
       "1     3571\n",
       "Name: left, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_l.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.8 promotion_last_5years列分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_pl5 = df['promotion_last_5years']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    14680\n",
       "1      319\n",
       "Name: promotion_last_5years, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_pl5.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.9 salary列分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_s = df['salary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "low       7316\n",
       "medium    6446\n",
       "high      1237\n",
       "Name: salary, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_s.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_s = s_s.where(s_s != 'nme').dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "low       7316\n",
       "medium    6446\n",
       "high      1237\n",
       "Name: salary, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_s.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.10 department列分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_d = df['department']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sales          0.276018\n",
       "technical      0.181345\n",
       "support        0.148610\n",
       "IT             0.081805\n",
       "product_mng    0.060137\n",
       "marketing      0.057204\n",
       "RandD          0.052470\n",
       "accounting     0.051137\n",
       "hr             0.049270\n",
       "management     0.042003\n",
       "Name: department, dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_d.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_d = s_d.where(s_d!='sale').dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sales          4140\n",
       "technical      2720\n",
       "support        2229\n",
       "IT             1227\n",
       "product_mng     902\n",
       "marketing       858\n",
       "RandD           787\n",
       "accounting      767\n",
       "hr              739\n",
       "management      630\n",
       "Name: department, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_d.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.对比分析 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna(axis=0, how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['last_evaluation']<=1][df['salary']!=\"nme\"][df[\"department\"]!=\"sale\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 14999 entries, 0 to 14998\n",
      "Data columns (total 10 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   satisfaction_level     14999 non-null  float64\n",
      " 1   last_evaluation        14999 non-null  float64\n",
      " 2   number_project         14999 non-null  int64  \n",
      " 3   average_monthly_hours  14999 non-null  int64  \n",
      " 4   time_spend_company     14999 non-null  int64  \n",
      " 5   Work_accident          14999 non-null  int64  \n",
      " 6   left                   14999 non-null  int64  \n",
      " 7   promotion_last_5years  14999 non-null  int64  \n",
      " 8   department             14999 non-null  object \n",
      " 9   salary                 14999 non-null  object \n",
      "dtypes: float64(2), int64(6), object(2)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_monthly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>department</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>0.618142</td>\n",
       "      <td>0.716830</td>\n",
       "      <td>3.816626</td>\n",
       "      <td>202.215974</td>\n",
       "      <td>3.468623</td>\n",
       "      <td>0.133659</td>\n",
       "      <td>0.222494</td>\n",
       "      <td>0.002445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandD</th>\n",
       "      <td>0.619822</td>\n",
       "      <td>0.712122</td>\n",
       "      <td>3.853875</td>\n",
       "      <td>200.800508</td>\n",
       "      <td>3.367217</td>\n",
       "      <td>0.170267</td>\n",
       "      <td>0.153748</td>\n",
       "      <td>0.034307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accounting</th>\n",
       "      <td>0.582151</td>\n",
       "      <td>0.717718</td>\n",
       "      <td>3.825293</td>\n",
       "      <td>201.162973</td>\n",
       "      <td>3.522816</td>\n",
       "      <td>0.125163</td>\n",
       "      <td>0.265971</td>\n",
       "      <td>0.018253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hr</th>\n",
       "      <td>0.598809</td>\n",
       "      <td>0.708850</td>\n",
       "      <td>3.654939</td>\n",
       "      <td>198.684709</td>\n",
       "      <td>3.355886</td>\n",
       "      <td>0.120433</td>\n",
       "      <td>0.290934</td>\n",
       "      <td>0.020298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>management</th>\n",
       "      <td>0.621349</td>\n",
       "      <td>0.724000</td>\n",
       "      <td>3.860317</td>\n",
       "      <td>201.249206</td>\n",
       "      <td>4.303175</td>\n",
       "      <td>0.163492</td>\n",
       "      <td>0.144444</td>\n",
       "      <td>0.109524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marketing</th>\n",
       "      <td>0.618601</td>\n",
       "      <td>0.715886</td>\n",
       "      <td>3.687646</td>\n",
       "      <td>199.385781</td>\n",
       "      <td>3.569930</td>\n",
       "      <td>0.160839</td>\n",
       "      <td>0.236597</td>\n",
       "      <td>0.050117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_mng</th>\n",
       "      <td>0.619634</td>\n",
       "      <td>0.714756</td>\n",
       "      <td>3.807095</td>\n",
       "      <td>199.965632</td>\n",
       "      <td>3.475610</td>\n",
       "      <td>0.146341</td>\n",
       "      <td>0.219512</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales</th>\n",
       "      <td>0.614447</td>\n",
       "      <td>0.709717</td>\n",
       "      <td>3.776329</td>\n",
       "      <td>200.911353</td>\n",
       "      <td>3.534058</td>\n",
       "      <td>0.141787</td>\n",
       "      <td>0.244928</td>\n",
       "      <td>0.024155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>0.618300</td>\n",
       "      <td>0.723109</td>\n",
       "      <td>3.803948</td>\n",
       "      <td>200.758188</td>\n",
       "      <td>3.393001</td>\n",
       "      <td>0.154778</td>\n",
       "      <td>0.248991</td>\n",
       "      <td>0.008973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technical</th>\n",
       "      <td>0.607897</td>\n",
       "      <td>0.721099</td>\n",
       "      <td>3.877941</td>\n",
       "      <td>202.497426</td>\n",
       "      <td>3.411397</td>\n",
       "      <td>0.140074</td>\n",
       "      <td>0.256250</td>\n",
       "      <td>0.010294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             satisfaction_level  last_evaluation  number_project  \\\n",
       "department                                                         \n",
       "IT                     0.618142         0.716830        3.816626   \n",
       "RandD                  0.619822         0.712122        3.853875   \n",
       "accounting             0.582151         0.717718        3.825293   \n",
       "hr                     0.598809         0.708850        3.654939   \n",
       "management             0.621349         0.724000        3.860317   \n",
       "marketing              0.618601         0.715886        3.687646   \n",
       "product_mng            0.619634         0.714756        3.807095   \n",
       "sales                  0.614447         0.709717        3.776329   \n",
       "support                0.618300         0.723109        3.803948   \n",
       "technical              0.607897         0.721099        3.877941   \n",
       "\n",
       "             average_monthly_hours  time_spend_company  Work_accident  \\\n",
       "department                                                              \n",
       "IT                      202.215974            3.468623       0.133659   \n",
       "RandD                   200.800508            3.367217       0.170267   \n",
       "accounting              201.162973            3.522816       0.125163   \n",
       "hr                      198.684709            3.355886       0.120433   \n",
       "management              201.249206            4.303175       0.163492   \n",
       "marketing               199.385781            3.569930       0.160839   \n",
       "product_mng             199.965632            3.475610       0.146341   \n",
       "sales                   200.911353            3.534058       0.141787   \n",
       "support                 200.758188            3.393001       0.154778   \n",
       "technical               202.497426            3.411397       0.140074   \n",
       "\n",
       "                 left  promotion_last_5years  \n",
       "department                                    \n",
       "IT           0.222494               0.002445  \n",
       "RandD        0.153748               0.034307  \n",
       "accounting   0.265971               0.018253  \n",
       "hr           0.290934               0.020298  \n",
       "management   0.144444               0.109524  \n",
       "marketing    0.236597               0.050117  \n",
       "product_mng  0.219512               0.000000  \n",
       "sales        0.244928               0.024155  \n",
       "support      0.248991               0.008973  \n",
       "technical    0.256250               0.010294  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"department\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>last_evaluation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>department</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>0.716830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandD</th>\n",
       "      <td>0.712122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accounting</th>\n",
       "      <td>0.717718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hr</th>\n",
       "      <td>0.708850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>management</th>\n",
       "      <td>0.724000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marketing</th>\n",
       "      <td>0.715886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_mng</th>\n",
       "      <td>0.714756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales</th>\n",
       "      <td>0.709717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>0.723109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technical</th>\n",
       "      <td>0.721099</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             last_evaluation\n",
       "department                  \n",
       "IT                  0.716830\n",
       "RandD               0.712122\n",
       "accounting          0.717718\n",
       "hr                  0.708850\n",
       "management          0.724000\n",
       "marketing           0.715886\n",
       "product_mng         0.714756\n",
       "sales               0.709717\n",
       "support             0.723109\n",
       "technical           0.721099"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, ['last_evaluation', 'department']].groupby(\"department\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "department\n",
       "IT             212\n",
       "RandD          210\n",
       "accounting     213\n",
       "hr             212\n",
       "management     210\n",
       "marketing      214\n",
       "product_mng    212\n",
       "sales          214\n",
       "support        214\n",
       "technical      213\n",
       "Name: average_monthly_hours, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, [\"average_monthly_hours\", \"department\"]].groupby(\"department\")[\"average_monthly_hours\"].apply(lambda x: x.max() - x.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.数据可视化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 柱状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"SALARY\")\n",
    "plt.bar(np.arange(len(df['salary'].value_counts()))+0.5, df['salary'].value_counts(), width=0.5)\n",
    "plt.xlabel(\"salry\")\n",
    "plt.ylabel(\"number\")\n",
    "plt.xticks(np.arange(len(df['salary'].value_counts()))+0.5, df['salary'].value_counts().index)  # 为刻度打上标签\n",
    "plt.axis([0,4,0,10000])\n",
    "\n",
    "# 标注出值的位置\n",
    "for x,y in zip(np.arange(len(df['salary'].value_counts()))+0.5, df['salary'].value_counts()):\n",
    "    plt.text(x, y, y, ha=\"center\",va=\"bottom\") # 第一个y是纵坐标位置，第二个y是要标注的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'SALARY')"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['salary'].value_counts().plot(kind=\"bar\")\n",
    "ax = plt.gca()\n",
    "ax.set_title(\"SALARY\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style(style=\"darkgrid\")\n",
    "sns.set_context(context=\"paper\", font_scale=1)  # 设置字体\n",
    "sns.set_palette(\"summer\")  # 设置一个调色板"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='salary', ylabel='count'>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"salary\", hue=\"department\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 直方图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "直方图和柱状图是不同的。   \n",
    "柱状图的x轴是类别，有意义的是柱子的高度   \n",
    "直方图的x轴是一个范围，有意义的是柱子的面积   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\virtualenvs\\py38jupyter\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "d:\\virtualenvs\\py38jupyter\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "d:\\virtualenvs\\py38jupyter\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(16, 8))\n",
    "f.add_subplot(1,3,1)\n",
    "sns.distplot(df['satisfaction_level'], bins=10, hist=True, kde=True)\n",
    "f.add_subplot(1,3,2)\n",
    "sns.distplot(df['last_evaluation'], bins=10, hist=True, kde=True)\n",
    "f.add_subplot(1,3,3)\n",
    "sns.distplot(df['average_monthly_hours'], bins=10, hist=True, kde=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 箱线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=df['time_spend_company'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['time_spend_company'], saturation=0.75, whis=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 折线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_monthly_hours</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_spend_company</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.697078</td>\n",
       "      <td>0.717596</td>\n",
       "      <td>3.687423</td>\n",
       "      <td>200.133169</td>\n",
       "      <td>0.172010</td>\n",
       "      <td>0.016338</td>\n",
       "      <td>0.016646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.626314</td>\n",
       "      <td>0.668721</td>\n",
       "      <td>3.327798</td>\n",
       "      <td>186.632935</td>\n",
       "      <td>0.138910</td>\n",
       "      <td>0.246159</td>\n",
       "      <td>0.020798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.467517</td>\n",
       "      <td>0.767927</td>\n",
       "      <td>4.627689</td>\n",
       "      <td>223.455221</td>\n",
       "      <td>0.124364</td>\n",
       "      <td>0.348064</td>\n",
       "      <td>0.013688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.610305</td>\n",
       "      <td>0.813666</td>\n",
       "      <td>4.519348</td>\n",
       "      <td>222.978955</td>\n",
       "      <td>0.116090</td>\n",
       "      <td>0.565513</td>\n",
       "      <td>0.011541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.603440</td>\n",
       "      <td>0.754875</td>\n",
       "      <td>4.213092</td>\n",
       "      <td>212.051532</td>\n",
       "      <td>0.149025</td>\n",
       "      <td>0.291086</td>\n",
       "      <td>0.023677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.635957</td>\n",
       "      <td>0.682766</td>\n",
       "      <td>3.851064</td>\n",
       "      <td>200.744681</td>\n",
       "      <td>0.138298</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.191489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.665062</td>\n",
       "      <td>0.711975</td>\n",
       "      <td>3.777778</td>\n",
       "      <td>193.802469</td>\n",
       "      <td>0.271605</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.061728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.655327</td>\n",
       "      <td>0.731495</td>\n",
       "      <td>3.682243</td>\n",
       "      <td>199.224299</td>\n",
       "      <td>0.233645</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.074766</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    satisfaction_level  last_evaluation  number_project  \\\n",
       "time_spend_company                                                        \n",
       "2                             0.697078         0.717596        3.687423   \n",
       "3                             0.626314         0.668721        3.327798   \n",
       "4                             0.467517         0.767927        4.627689   \n",
       "5                             0.610305         0.813666        4.519348   \n",
       "6                             0.603440         0.754875        4.213092   \n",
       "7                             0.635957         0.682766        3.851064   \n",
       "8                             0.665062         0.711975        3.777778   \n",
       "10                            0.655327         0.731495        3.682243   \n",
       "\n",
       "                    average_monthly_hours  Work_accident      left  \\\n",
       "time_spend_company                                                   \n",
       "2                              200.133169       0.172010  0.016338   \n",
       "3                              186.632935       0.138910  0.246159   \n",
       "4                              223.455221       0.124364  0.348064   \n",
       "5                              222.978955       0.116090  0.565513   \n",
       "6                              212.051532       0.149025  0.291086   \n",
       "7                              200.744681       0.138298  0.000000   \n",
       "8                              193.802469       0.271605  0.000000   \n",
       "10                             199.224299       0.233645  0.000000   \n",
       "\n",
       "                    promotion_last_5years  \n",
       "time_spend_company                         \n",
       "2                                0.016646  \n",
       "3                                0.020798  \n",
       "4                                0.013688  \n",
       "5                                0.011541  \n",
       "6                                0.023677  \n",
       "7                                0.191489  \n",
       "8                                0.061728  \n",
       "10                               0.074766  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 绘制，随着在公司的时间越长，他的离职率是怎样的？\n",
    "sub_df = df.groupby(\"time_spend_company\").mean()\n",
    "sub_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='time_spend_company', ylabel='left'>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=sub_df.index, y=sub_df['left'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='time_spend_company', ylabel='left'>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pointplot(x=sub_df.index, y=sub_df['left'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 直接使用原始数据绘制\n",
    "sns.pointplot(x=\"time_spend_company\", y=\"left\", data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5饼图\n",
    "seaborn中没有饼图的画法，用matplotlib画。  \n",
    "饼图主要做结构分析，所以都是一些类别  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,10))\n",
    "lbs = df['department'].value_counts(normalize=True).index\n",
    "explodes =[0.1 if d==\"sales\" else 0 for d in lbs]\n",
    "plt.pie(df['department'].value_counts(normalize=True), labels=lbs, explode=explodes, autopct=\"%1.1f%%\", colors=sns.color_palette(\"Reds\"))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-119-e7700c221d7c>:4: MatplotlibDeprecationWarning: normalize=None does not normalize if the sum is less than 1 but this behavior is deprecated since 3.3 until two minor releases later. After the deprecation period the default value will be normalize=True. To prevent normalization pass normalize=False \n",
      "  plt.pie(df['salary'].value_counts(normalize=True), labels=lbs, explode=explodes, autopct=\"%1.1f%%\", colors=sns.color_palette(\"Reds\"))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,10))\n",
    "lbs = df['salary'].value_counts(normalize=True).index\n",
    "explodes =[0.1 if d==\"low\" else 0 for d in lbs]\n",
    "plt.pie(df['salary'].value_counts(normalize=True), labels=lbs, explode=explodes, autopct=\"%1.1f%%\", colors=sns.color_palette(\"Reds\"))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.小结"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可视化的内容比较丰富，还有气泡图，散点图，雷达图等等。  \n",
    "可以边看文档边学习  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_monthly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>department</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11009</th>\n",
       "      <td>0.81</td>\n",
       "      <td>0.55</td>\n",
       "      <td>4</td>\n",
       "      <td>217</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>accounting</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "11009                0.81             0.55               4   \n",
       "\n",
       "       average_monthly_hours  time_spend_company  Work_accident  left  \\\n",
       "11009                    217                   8              0     0   \n",
       "\n",
       "       promotion_last_5years  department  salary  \n",
       "11009                      0  accounting  medium  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "拿到一条数据，我们要分析的是，这条数据的各个属性，相对于总体来说的，处于一种什么位置。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
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 "nbformat_minor": 4
}
